Correlation analysis of vegetation indices based on multi-temporal satellite images and unmanned aerial vehicle images with wheat protein contents

Abstract

Satellite remote sensing has been proved to be an effective way of monitoring crop growth status and yield prediction. Recently near-ground remote sensing using an unmanned aerial vehicle (UAV) witnessed wide applications in obtaining field information. In this research, four Rapideye satellite images and eight UAV images are used from early June to the end of July 2015, covering two different varieties of winter wheat fields, in order to analyze the correlation among satellite images based normalized difference vegetation index (NDVI) with the UAV images based visible-band difference vegetation index (VDVI) and ground variable of grain protein contents. The results of relational analysis of NDVI with sampled wheat grain protein content showed that NDVI related most to the grain protein content at the later stage of wheat growing season, one week prior to harvesting. And the linear-regression models for field No.1 and field No.2 of NDVI with VDVI showed good consistency at the early stage of wheat growing season, with the coefficient of determination R2 = 0.62 and 0.86, respectively.